1. Disease Growth rate

Firstly, I explored the growth rate of the disease throughout Indonesia using a time series. This was done to find out how fast the disease was spreading and whether government intervention (implemented at certain dates) effectively contained the virus.

#Read Time-series data
time <- read.csv("Timeseries.csv")

#Convert Date column into date format
time$Date <- ymd(time$Date)

#Plot growth rate
plot_ly(time,
        x=~Date,
        y=~New_case_per_day,
        name="New Confirmed cases",
        type="scatter",
        mode="lines+markers")%>%
  add_trace(y=~Death_cases_perDay, name="New Death cases", mode="lines+markers")%>%
  layout(title="Daily Growth rate of COVID-19 in Indonesia",
         xaxis=list(title="Date"),
         yaxis=list(title="Number of New Cases"))

2. Status of cumulative cases

Next, I analyzed the proportion of recoveries and deaths in relation to the cumulative amount of cases. Using an area plot, we can see the proportion of the infected population that has recovered, died, or are still currently under treatment.

plot_ly(time,
        x=~Date,
        y=~Patient_under_treatment,
        name="Cases under treatment",
        type="scatter",
        mode="none",
        stackgroup='one',
        fillcolor="steel blue",
        hoverinfo="text",
        text=~paste(Date, " Percentage: ", as.character(round(((Patient_under_treatment/Cumulative_cases)*100), digits=2)),'%'))%>%
  add_trace(y = ~Recovered_cases, name = 'Recovered cases', fillcolor = 'chartreuse',hoverinfo="text",
            text=~paste(Date, " Percentage: ", as.character(round(((Recovered_cases/Cumulative_cases)*100), digits=2)),'%'))%>%
  add_trace(y=~Total_death, name= 'Death cases', fillcolor='orange', hoverinfo="text",
            text=~paste(Date, " Percentage: ", as.character(round(((Total_death/Cumulative_cases)*100), digits=2)),'%'))%>%
  layout(title="Cumulative cases of COVID-19 in Indonesia",
         xaxis=list(title="Date"),
         yaxis=list(title="Number of Cases"))
## Warning: Ignoring 8 observations
## Warning: Ignoring 9 observations

3. Geographic distribution of cases

To analyze how the disease has spread throughout the country, I created a choropleth showing the total number of confirmed cases in each province.

#Read Provincial COVID-19 distribution data
prov <- read.csv("CasebyProvince.csv")

#Read provincial map of Indonesia
province <- geojson_read("IDN_adm_1_province.json", what='sp')

#Prepare data frames for merging the two loaded datasets
slotNames(province)
## [1] "data"        "polygons"    "plotOrder"   "bbox"        "proj4string"
province@data$rn <- row.names(province)

temp.province<-data.table(province@data)

#Fix names of provinces according to those in the map data
prov %>%
  mutate(NAME_1=as.character(Province_name)) -> prov
prov%>%
  mutate(NAME_1=ifelse(NAME_1=="Daerah Istimewa Yogyakarta", 'Yogyakarta', NAME_1),
         NAME_1=ifelse(NAME_1=="DKI Jakarta", 'Jakarta Raya', NAME_1),
         NAME_1=ifelse(NAME_1=="Papua Barat", 'Irian Jaya Barat', NAME_1),
         NAME_1=ifelse(NAME_1=="Kepulauan Bangka Belitung", 'Bangka-Belitung', NAME_1)) -> prov
prov <- prov %>% filter(NAME_1 != 'Indonesia'&NAME_1 !='Kepulauan Riau')

#Merge two datasets
out.prov<-merge(temp.province, prov, by="NAME_1", all.x=TRUE)

out.prov<-data.table(out.prov)

setkey(out.prov, rn)
province@data <- out.prov[row.names(province)]

#Define coloring function and color palette
pal <- colorNumeric("YlOrRd", domain = NULL)

#Plot confirmed cases choropleth
leaflet(data = province)%>%
  addProviderTiles("CartoDB.Positron")%>%
  addPolygons(fillColor = ~pal(log(Confirmed_cases)),
              fillOpacity = 0.8,
              color = "black",
              weight = 1,
              popup=~paste(NAME_1, ", No. of Confirmed cases:", as.character(Confirmed_cases)))%>%
  addMarkers(lng=106.816666,
             lat=-6.200000,
             popup=paste("Jakarta", ", No. of Confirmed cases: 5688"))%>%
  addLegend("bottomleft",
            colors=brewer.pal(5,"YlOrRd"),
            labels=c("low","","","","high"),
            title="Number of Confirmed cases")

4. Fatality rates by province

I also looked into the case fatality rate of each province in Indonesia using a choropleth. Case fatality rate is the percentage of deaths over the total number of cases in that province.

leaflet(data = province)%>%
  addProviderTiles("CartoDB.Positron")%>%
  addPolygons(fillColor = ~pal((Death_cases/Confirmed_cases)*100),
              fillOpacity = 0.8,
              color = "black",
              weight = 1,
              popup=~paste(NAME_1, ", Case Fatality Rate:", as.character(round((Death_cases/Confirmed_cases)*100, digits=2)), '%'))%>%
  addMarkers(lng=106.816666,
             lat=-6.200000,
             popup=paste("Jakarta", ", Case Fatality Rate: 7.95%"))%>%
  addLegend("bottomleft",
            colors=brewer.pal(5,"YlOrRd"),
            labels=c("low","","","","high"),
            title="Case Fatality rate")

5. Comparison between countries and world average

I compared Indonesia’s case fatality rate over time with the world average, Asia’s Average, the US, and Italy. The dataset chosen only records case fatality rate starting on March 16th, 2020, which is when the number of cases reached 100 in Indonesia.

#Read and fix structure of columns in the global death rate dataset
w_death <- read.csv("deathrate.csv")
w_death$Entity <- as.character(w_death$Entity)
w_death$Date <- mdy(w_death$Date)

#Filter to regions and dates of interest
w_death <- w_death %>% filter(Entity %in% c("World", "United States", "Indonesia", "Asia", "Italy")&
                                Date>=ymd("2020-03-16")&Date<=ymd("2020-05-14"))

#Plot death rate data of different regions
ggplot(w_death)+
  geom_line(aes(x=Date, y=Case.fatality.rate.of.COVID.19......Only.observations.with.â..100.cases....., color=Entity))+
  ggtitle("Case Fatality rate in different countries as of May 14th 2020")+
  ylab("Case Fatality rate (%)")

Literary Sources:

Indonesia Age structure. (n.d.). Retrieved from https://www.indexmundi.com/indonesia/age_structure.html

Jakarta Post. (n.d.). Indonesia’s latest official COVID-19 figures. Retrieved from https://www.thejakartapost.com/news/2020/03/23/indonesias-latest-covid-19-figures.html

Policy Responses to COVID19. (n.d.). Retrieved from https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19